Introduction


Genomics studies employ multiple independent lines of investigation to address a phenotype or complex genetic trait. This includes studying various forms of genomic variation (SNPs, CNVs, InDels) and gene expression (in multiple tissues) in a single phenotype. In addition, such studies might be carried out in a single or multiple species of interest (e.g, humans and other relevant model organisms). One of the common characteristics of such modern high-throughput experiments across -omics fields is that they produce long lists of genes. Integration of data at gene-level from multiple evidence layers has been shown to be an effective approach to identify and prioritize candidate genes in complex genetic traits. Here, we have implemented three methods to integrate gene-level data generated from multiple independent lines of investigation (Figure 1):

Figure 1: Overarching goal of the package

Figure 1: Overarching goal of the package


Background


Evidence layers

We mentioned about the integration of gene-level data from multiple evidence layers above. Here, we briefly explain what is referred to as an ‘evidence layer’ throughout this package. An evidence layer could be one of the multiple independent lines of investigation. Those independent lines of investigation may use a same method (e.g GWAS) to study the phenotype in independent sample groups (e.g GWAS studies carried out by different labs to study the same phenotype). Alternatively, the independent lines of investigation may use different methods (e.g SNP, CNV, RNA, miRNA) to study the phenotype in same or independent sample groups. Instead, the independent lines of investigation may employ multiple methods to study the same phenotype in different tissues or altogether in different species. However, the definition of phenotype and phenotypic homogeneity (less variability in phenotypic characterization) is very crucial in this kind of integrative studies. Examples of evidence layers are shown below in Figure 2.